Parkinson’s Disease scRNA-seq of CD14+ Cells

Setup

## Automatically generates rmarkdown output file

# Subset gene biotypes to be analyzed
subsetGenes="protein_coding"#NULL
# Specify the resolution of the unsupervised clustering algorithm
resolution=1.0 
# Subset cells
if (getwd()!="/Users/schilder/Desktop/PD_scRNAseq"){
  subsetCells=500
} else {subsetCells=NULL}
 


# params <- list(set_title=paste(sep="", "PDscRNAseq__",
#                 "Genes-",subsetGenes,"__Cells-",subsetCells,"__Resolution-",resolution,
#                 ".html"))  
kableStyle = c("striped", "hover", "condensed", "responsive")
knitr::opts_chunk$set(echo=T, error=T, cache=T, cache.lazy=F) 
 
# rmarkdown::render(input = "run_seurat.Rmd", output_file = params$set_title,   output_format = "html_document")   

nCores = parallel::detectCores()
print(paste("**** Utilized Cores **** =", parallel::detectCores() )) 

## [1] "**** Utilized Cores **** = 4"

Load Libraries & Report Versions

library(Seurat)
library(dplyr)
library(gridExtra)
library(knitr)
library(kableExtra) 
## Install Bioconductor
#  if (!requireNamespace("BiocManager"))
#     install.packages("BiocManager")
# BiocManager::install(c("biomaRt"))
library(biomaRt) 

sessionInfo()

## R version 3.5.1 (2018-07-02)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS  10.14.1
## 
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] kableExtra_0.9.0 knitr_1.21       readxl_1.2.0     bindrcpp_0.2.2  
##  [5] biomaRt_2.38.0   gridExtra_2.3    dplyr_0.7.8      Seurat_2.3.4    
##  [9] Matrix_1.2-15    cowplot_0.9.3    ggplot2_3.1.0   
## 
## loaded via a namespace (and not attached):
##   [1] Rtsne_0.15           colorspace_1.3-2     class_7.3-14        
##   [4] modeltools_0.2-22    ggridges_0.5.1       mclust_5.4.2        
##   [7] htmlTable_1.12       base64enc_0.1-3      rstudioapi_0.8      
##  [10] proxy_0.4-22         npsurv_0.4-0         flexmix_2.3-14      
##  [13] bit64_0.9-7          AnnotationDbi_1.44.0 mvtnorm_1.0-8       
##  [16] xml2_1.2.0           codetools_0.2-16     splines_3.5.1       
##  [19] R.methodsS3_1.7.1    lsei_1.2-0           robustbase_0.93-3   
##  [22] jsonlite_1.6         Formula_1.2-3        ica_1.0-2           
##  [25] cluster_2.0.7-1      kernlab_0.9-27       png_0.1-7           
##  [28] R.oo_1.22.0          readr_1.3.1          compiler_3.5.1      
##  [31] httr_1.4.0           backports_1.1.3      assertthat_0.2.0    
##  [34] lazyeval_0.2.1       prettyunits_1.0.2    lars_1.2            
##  [37] acepack_1.4.1        htmltools_0.3.6      tools_3.5.1         
##  [40] igraph_1.2.2         gtable_0.2.0         glue_1.3.0          
##  [43] reshape2_1.4.3       RANN_2.6             Rcpp_1.0.0          
##  [46] Biobase_2.42.0       cellranger_1.1.0     trimcluster_0.1-2.1 
##  [49] gdata_2.18.0         ape_5.2              nlme_3.1-137        
##  [52] iterators_1.0.10     fpc_2.1-11.1         gbRd_0.4-11         
##  [55] lmtest_0.9-36        xfun_0.4             stringr_1.3.1       
##  [58] rvest_0.3.2          irlba_2.3.2          gtools_3.8.1        
##  [61] XML_3.98-1.16        DEoptimR_1.0-8       MASS_7.3-51.1       
##  [64] zoo_1.8-4            scales_1.0.0         hms_0.4.2           
##  [67] doSNOW_1.0.16        parallel_3.5.1       RColorBrewer_1.1-2  
##  [70] yaml_2.2.0           memoise_1.1.0        reticulate_1.10     
##  [73] pbapply_1.3-4        rpart_4.1-13         segmented_0.5-3.0   
##  [76] RSQLite_2.1.1        latticeExtra_0.6-28  stringi_1.2.4       
##  [79] S4Vectors_0.20.1     foreach_1.4.4        checkmate_1.8.5     
##  [82] BiocGenerics_0.28.0  caTools_1.17.1.1     bibtex_0.4.2        
##  [85] Rdpack_0.10-1        SDMTools_1.1-221     rlang_0.3.0.1       
##  [88] pkgconfig_2.0.2      dtw_1.20-1           prabclus_2.2-6      
##  [91] bitops_1.0-6         evaluate_0.12        lattice_0.20-38     
##  [94] ROCR_1.0-7           purrr_0.2.5          bindr_0.1.1         
##  [97] htmlwidgets_1.3      bit_1.1-14           tidyselect_0.2.5    
## [100] plyr_1.8.4           magrittr_1.5         R6_2.3.0            
## [103] IRanges_2.16.0       snow_0.4-3           gplots_3.0.1        
## [106] Hmisc_4.1-1          DBI_1.0.0            pillar_1.3.1        
## [109] foreign_0.8-71       withr_2.1.2          fitdistrplus_1.0-11 
## [112] mixtools_1.1.0       RCurl_1.95-4.11      survival_2.43-3     
## [115] nnet_7.3-12          tsne_0.1-3           tibble_1.4.2        
## [118] crayon_1.3.4         hdf5r_1.0.1          KernSmooth_2.23-15  
## [121] rmarkdown_1.11       progress_1.2.0       grid_3.5.1          
## [124] data.table_1.11.8    blob_1.1.1           metap_1.0           
## [127] digest_0.6.18        diptest_0.75-7       tidyr_0.8.2         
## [130] R.utils_2.7.0        stats4_3.5.1         munsell_0.5.0       
## [133] viridisLite_0.3.0

print(paste("Seurat ", packageVersion("Seurat")))

## [1] "Seurat  2.3.4"

Load Data

#setwd("~/Desktop/PD_scRNAseq/")
dir.create(file.path("Data"), showWarnings=F) 
load("Data/seurat_object_add_HTO_ids.Rdata")
pbmc <- seurat.obj  
rm(seurat.obj) 
pbmc

## An object of class seurat in project RAJ_13357 
##  24914 genes across 22113 samples.

Clean Metadata

Add Metadata

metadata <- read.table("Data/meta.data4.tsv")  
kable(head(metadata), caption = "Metadata") %>%  
  kable_styling(bootstrap_options = kableStyle) %>%
  scroll_box(width = "100%", height = "500px")

Metadata

	ID	nGene	nUMI	orig.ident	singlet.or.not.binary	percent.mito	res.2	res.1	res.0.6	res.0.3	CellType	barcode	dx	mut	Ethnicity	Gender	Age
AAAGCAAGTTTGTTGG	BIMD0007	784	1780	RAJ_13357	1	0.0314607	3	3	1	0	0	AAAGCAAGTTTGTTGG	PD	PD	White	M	59
TCAGCAATCTTGACGA	BIMD0007	742	1854	RAJ_13357	1	0.0302213	18	1	0	0	0	TCAGCAATCTTGACGA	PD	PD	White	M	59
AGCTCCTTCGCGTAGC	BIMD0007	495	988	RAJ_13357	1	0.0404858	14	7	2	3	3	AGCTCCTTCGCGTAGC	PD	PD	White	M	59
TATTACCCACTCTGTC	BIMD0007	812	1874	RAJ_13357	1	0.0469584	16	12	7	6	6	TATTACCCACTCTGTC	PD	PD	White	M	59
CTCGAGGAGCGATTCT	BIMD0007	863	2260	RAJ_13357	1	0.0212578	1	0	1	0	0	CTCGAGGAGCGATTCT	PD	PD	White	M	59
ATAAGAGCATCAGTCA	BIMD0007	803	2034	RAJ_13357	1	0.0216323	9	8	0	0	0	ATAAGAGCATCAGTCA	PD	PD	White	M	59

# Make AgeGroups
makeAgeGroups <- function(){
  dim(metadata)
  getMaxRound <- function(vals=metadata$Age, unit=10)unit*ceiling((max(vals)/unit))
  getMinRound <- function(vals=metadata$Age, unit=10)unit*floor((min(vals)/unit)) 
   
  ageBreaks = c(seq(getMinRound(), getMaxRound(), by = 10), getMaxRound()+10)
  AgeGroupsUniq <- c()
  for (i in 1:(length(ageBreaks)-1)){ 
    AgeGroupsUniq <- append(AgeGroupsUniq, paste(ageBreaks[i],ageBreaks[i+1], sep="-")) 
  } 
  data.table::setDT(metadata,keep.rownames = T,check.names = F)[, AgeGroups := cut(Age, 
                                  breaks = ageBreaks, 
                                  right = F, 
                                  labels = AgeGroupsUniq,
                                  nclude.lowest=T)]
  metadata <- data.frame(metadata)
  unique(metadata$AgeGroups)
  head(metadata)
  dim(metadata)
  return(metadata)
}
# metadata <- makeAgeGroups()

pbmc <- AddMetaData(object = pbmc, metadata = metadata)  
# Get rid of any NAs (cells that don't match up with the metadata) 
cellLimiter <- ifelse(is.null(subsetCells), len(pbmc@cell.names), subsetCells)
pbmc <- FilterCells(object = pbmc,  subset.names = "nGene", low.thresholds = 0,
                    # Subset for testing 
                    cells.use = pbmc@cell.names[0:cellLimiter] 
                    )
pbmc

An object of class seurat in project RAJ_13357 24914 genes across 495 samples.

Filter & Normalize Data

Gene Biotypes

Include only subsets of genes by type. Biotypes from: https://useast.ensembl.org/info/genome/genebuild/biotypes.html

## Seurat::FindGeneTerms() # Enrichr API
## Seurat::MultiModal_CCA() # Integrates data from disparate datasets (CIA version too)
if(!is.null(subsetGenes)){
  # If the gene_biotypes file exists, import csv. Otherwise, get from biomaRt
  if(file_test("-f", "Data/gene_biotypes.csv")){
    biotypes <- read.csv("Data/gene_biotypes.csv") 
  }
  else {
    ensembl <- useMart(biomart="ENSEMBL_MART_ENSEMBL", host="grch37.ensembl.org",
                     dataset="hsapiens_gene_ensembl") 
    ensembl <- useDataset(mart = ensembl, dataset = "hsapiens_gene_ensembl")
    listFilters(ensembl)
    listAttributes(ensembl)   
    biotypes <- getBM(attributes=c("hgnc_symbol", "gene_biotype"), filters="hgnc_symbol",
          values=row.names(pbmc@data), mart=ensembl) 
    write.csv(biotypes, "Data/gene_biotypes.csv", quote=F, row.names=F)
  } 
  # Subset data by creating new Seurat object (annoying but necessary)
  geneSubset <- biotypes[biotypes$gene_biotype==subsetGenes,"hgnc_symbol"] 
  
  print(paste(dim(pbmc@raw.data[geneSubset, ])[1],"/", dim(pbmc@raw.data)[1], 
              "genes are", subsetGenes))
  # Add back into pbmc 
  subset.matrix <- pbmc@raw.data[geneSubset, ] # Pull the raw expression matrix from the original Seurat object containing only the genes of interest
  pbmc_sub <- CreateSeuratObject(subset.matrix) # Create a new Seurat object with just the genes of interest
  orig.ident <- row.names(pbmc@meta.data) # Pull the identities from the original Seurat object as a data.frame
  pbmc_sub <- AddMetaData(object = pbmc_sub, metadata = pbmc@meta.data) # Add the idents to the meta.data slot
  pbmc_sub <- SetAllIdent(object = pbmc_sub, id = "ident") # Assign identities for the new Seurat object
  pbmc <- pbmc_sub
  rm(pbmc_sub)
  pbmc
} 

## [1] "14827 / 24914 genes are protein_coding"

## An object of class seurat in project SeuratProject 
##  14827 genes across 27863 samples.

Filter Cells, Gene Variance, & Normalize

Filter by cells, normalize , filter by gene variability.
** Important!**: Specify do.par = T, and num.cores = nCores in ‘ScaleData’ to use all available cores.

pbmc <- FilterCells(object = pbmc, subset.names = c("nGene", "percent.mito"), 
    low.thresholds = c(200, -Inf), high.thresholds = c(2500, 0.05))

pbmc <- NormalizeData(object = pbmc, normalization.method = "LogNormalize", 
    scale.factor = 10000)

# Store the top most variable genes in @var.genes
pbmc <- FindVariableGenes(object = pbmc, mean.function = ExpMean, dispersion.function = LogVMR,
    x.low.cutoff = 0.0125, x.high.cutoff = 3, y.cutoff = 0.5)

# IMPORTANT!: Must set do.par=T and num.cors = n for large datasets being processed on computing clusters
pbmc <- ScaleData(object = pbmc, vars.to.regress = c("nUMI", "percent.mito"), do.par = T, num.cores = nCores)

## Regressing out: nUMI, percent.mito

## Warning in RegressOutResid(object = object, vars.to.regress =
## vars.to.regress, : num.cores set greater than number of available cores(4).
## Setting num.cores to 3.

## 
## Time Elapsed:  6.84275984764099 secs

## Scaling data matrix

Diagnostic Plots

Violin Plots

VlnPlot(object = pbmc, features.plot = c("nGene", "nUMI", "percent.mito"),nCol = 3)  

Gene Plots

percent.mito plot

# par(mfrow = c(1, 2))
gp1 <- GenePlot(object = pbmc, gene1 = "nUMI", gene2 = "percent.mito", pch.use=20, 
         do.hover=T, data.hover = "mut")

gp1

nGene plot

gp2 <- GenePlot(object = pbmc, gene1 = "nUMI", gene2 = "nGene", pch.use=20, 
         do.hover=T, data.hover = "mut")

gp2

Dimensionality Reduction

PCA

ProjectPCA scores each gene in the dataset (including genes not included in the PCA) based on their correlation with the calculated components. Though we don’t use this further here, it can be used to identify markers that are strongly correlated with cellular heterogeneity, but may not have passed through variable gene selection. The results of the projected PCA can be explored by setting use.full=T in the functions above

• Other Dim Reduction Methods in Seurat
• RunCCA()
• RunMultiCCA()
• RunDiffusion()
• RunPHATE()
• RunUMAP()
• RunICA()

# Run PCA with only the top most variables genes
pbmc <- RunPCA(object = pbmc, pc.genes = pbmc@var.genes, do.print=F)
  #, pcs.print = 1:5,  genes.print = 5

VizPCA

VizPCA(object = pbmc, pcs.use = 1:2)

PCA plot

PCAPlot(object = pbmc, dim.1 = 1, dim.2 = 2)

PCHeatmap

pbmc <- ProjectPCA(object = pbmc, do.print=F)

## PCA Heatmap: PC1
PCHeatmap(object = pbmc, pc.use = 1, cells.use = 500, do.balanced=T, label.columns=F)

## PCA Heatmap: PC1-PCn
PCHeatmap(object = pbmc, pc.use = 1:12, cells.use = 500, do.balanced=T, 
    label.columns=F, use.full=F)

Significant PCs

Determine statistically significant PCs for further analysis. NOTE: This process can take a long time for big datasets, comment out for expediency. More approximate techniques such as those implemented in PCElbowPlot() can be used to reduce computation time

#pbmc <- JackStraw(object = pbmc, num.replicate = 100, display.progress = FALSE)
PCElbowPlot(object = pbmc)

Find Cell Clusters

We first construct a KNN graph based on the euclidean distance in PCA space, and refine the edge weights between any two cells based on the shared overlap in their local neighborhoods (Jaccard similarity). To cluster the cells, we apply modularity optimization techniques such as the Louvain algorithm (default) or SLM [SLM, Blondel et al., Journal of Statistical Mechanics], to iteratively group cells together, with the goal of optimizing the standard modularity function.

On Resolution
The FindClusters function implements the procedure, and contains a resolution parameter that sets the ‘granularity’ of the downstream clustering, with increased values leading to a greater number of clusters. We find that setting this parameter between 0.6-1.2 typically returns good results for single cell datasets of around 3K cells. Optimal resolution often increases for larger datasets. The clusters are saved in the object@ident slot.

# TRY DIFFERENT RESOLUTIONS
pbmc <- FindClusters(object = pbmc, reduction.type = "pca", dims.use = 1:10, 
    resolution = resolution, print.output = 0, save.SNN = T) 
PrintFindClustersParams(object = pbmc) 

## Parameters used in latest FindClusters calculation run on: 2019-01-03 14:42:52
## =============================================================================
## Resolution: 0.6
## -----------------------------------------------------------------------------
## Modularity Function    Algorithm         n.start         n.iter
##      1                   1                 100             10
## -----------------------------------------------------------------------------
## Reduction used          k.param          prune.SNN
##      pca                 30                0.0667
## -----------------------------------------------------------------------------
## Dims used in calculation
## =============================================================================
## 1 2 3 4 5 6 7 8 9 10

t-SNE

As input to the tSNE, we suggest using the same PCs as input to the clustering analysis, although computing the tSNE based on scaled gene expression is also supported using the genes.use argument.

** Important!**: Specify num_threads=0 in ‘RunTSNE’ to use all available cores.

labSize <- 6
#pbmc <- StashIdent(object = pbmc, save.name = "pre_clustering") 
#pbmc <- SetAllIdent(object = pbmc, id = "pre_clustering") 

pbmc <- RunTSNE(object=pbmc,  reduction.use = "pca", dims.use = 1:10, do.fast = TRUE, 
                tsne.method = "Rtsne", num_threads=0) # num_threads
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = pbmc, do.label=T, label.size = labSize) 

saveRDS(pbmc, file = "Data/cd14-processed.rds") 

t-SNE + Metadata Plots

tSNE_metadata_plot <- function(var){
  print(paste("t-SNE Metadata plot for ", var))
  # Metadata plot 
  p1 <- TSNEPlot(pbmc, do.return = T, pt.size = 0.5, group.by = var, do.label = T, 
                 dark.theme=F, plot.title=paste("Color by ",var), vector.friendly=T)
  # t-SNE clusters plot
  p2 <- TSNEPlot(pbmc, do.label = T, do.return = T, pt.size = 0.5, plot.title=paste("Color by Unsupervised Clusters"), vector.friendly=T)
  print(plot_grid(p1, p2))
}   
# metaVars <- c("CellType","dx","mut","Gender","Age")
# 
# for (var in metaVars){
#   print(paste("t-SNE Metadata plot for ",var))
#   # Metadata plot 
#   p1 <- TSNEPlot(pbmc, do.return = T, pt.size = 0.5, group.by = var, do.label = T, 
#                  dark.theme=F, plot.title=paste("Color by ",var))
#   # t-SNE clusters plot
#   p2 <- TSNEPlot(pbmc, do.label = T, do.return = T, pt.size = 0.5, plot.title=paste("Color by t-SNE clusters"))
#   print(plot_grid(p1, p2))
# }   

CellType

tSNE_metadata_plot("CellType") 

## [1] "t-SNE Metadata plot for  CellType"

Disease

tSNE_metadata_plot("dx") 

## [1] "t-SNE Metadata plot for  dx"

Mutations

tSNE_metadata_plot("mut") 

## [1] "t-SNE Metadata plot for  mut"

Gender

tSNE_metadata_plot("Gender") 

## [1] "t-SNE Metadata plot for  Gender"

Age

tSNE_metadata_plot("Age") 

## [1] "t-SNE Metadata plot for  Age"

Cluster Biomarkers

Seurat has several tests for differential expression which can be set with the test.use parameter (see the DE vignette for details). For example, the ROC test returns the ‘classification power’ for any individual marker (ranging from 0 - random, to 1 - perfect).

### Biomarkers: One Cluster vs. Specific Clusters
# cluster5.markers <- FindMarkers(object = pbmc, ident.1 = 0, ident.2 = c(2), 
#     min.pct = 0.25)
# print(x = head(x = cluster5.markers, n = 3)) 

### Biomarkers: One Cluster vs. All Other Clusters 
# find all markers of a given cluster
# MUST run FindClusters() first
# cluster0.markers <- FindMarkers(object = pbmc, ident.1 = 0, min.pct = 0.25)
# print(x = head(x = cluster0.markers, n = 3))   


### Biomarkers: All Clusters vs. All Other Clusters ***
# find markers for every cluster compared to all remaining cells, report
# only the positive ones
pbmc.markers <- FindAllMarkers(object = pbmc, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
topBiomarkers <- pbmc.markers %>% group_by(cluster) %>% top_n(2, avg_logFC)

kable(topBiomarkers) %>% kable_styling(bootstrap_options = kableStyle)

p_val	avg_logFC	pct.1	pct.2	p_val_adj	cluster	gene
0	0.9939484	1.000	1.000	0	0	S100A8
0	1.0147286	0.938	0.692	0	0	S100A12
0	1.3719797	0.727	0.168	0	1	FCGR3A
0	1.3581437	0.388	0.086	0	1	C1QA
0	2.1004198	0.784	0.069	0	2	FCER1A
0	1.4090180	0.664	0.140	0	2	CLEC10A

Cluster Biomarker Tests

getTopBiomarker <- function(pbmc.markers, clusterID, topN=1){
  df <- subset(pbmc.markers, p_val_adj<0.05 & cluster==as.character(clusterID)) %>% arrange(desc(avg_logFC))
  top_pct_markers <- df[1:topN,"gene"]
  return(top_pct_markers)
}
# clust1_biomarkers <- getTopBiomarker(pbmc.markers, clusterID=1, topN=2)
# clust2_biomarkers <- getTopBiomarker(pbmc.markers, clusterID=2, topN=2)


### Plot biomarkers 
plotBiomarkers <- function(pbmc, biomarkers, cluster){
  biomarkerPlots <- list()
  for (marker in biomarkers){
    #print(marker)
    p <- VlnPlot(object = pbmc, features.plot = c(marker), y.log=T, return.plotlist=T)
    biomarkerPlots[[marker]] <- p + ggplot2::aes(alpha=.7) 
  }
  combinedPlot <- do.call(grid.arrange, c(biomarkerPlots, list(ncol=2, top=paste("Top DEG Biomarkers for Cluster",cluster))) )
  return(combinedPlot) 
} 

# Plot top 2 biomarker genes for each 
for (clust in unique(pbmc.markers$cluster)){ 
   cat('\n')   
   cat("### Cluster ",clust,"\n") 
   biomarkers <- getTopBiomarker(pbmc.markers, clusterID=clust, topN=2)
   plotBiomarkers(pbmc, biomarkers, clust)
   cat('\n')   
} 

Cluster 0

Cluster 1

Cluster 2

Top Biomarker Plots

top1 <- pbmc.markers %>% group_by(cluster) %>% top_n(1, avg_logFC)

nCols = round( length(unique(top1$cluster)) / 3 ) 
figHeight <- nCols*7

tSNE

FeaturePlot(object = pbmc, features.plot = top1$gene, cols.use = c("grey", "blue"), 
    reduction.use = "tsne", nCol = nCols)

Heatmap

top10 <- pbmc.markers %>% group_by(cluster) %>% top_n(10, avg_logFC)
# setting slim.col.label to TRUE will print just the cluster IDS instead of
# every cell name
DoHeatmap(object = pbmc, genes.use = top10$gene, slim.col.label=T, remove.key=T)

Ridgeplot

RidgePlot(pbmc, features.plot = top1$gene,  nCol = nCols, do.sort = F)

## Picking joint bandwidth of 0.298

## Picking joint bandwidth of 0.117

## Picking joint bandwidth of 0.12

Map Clusters to Cell Types

Label Clusters by Biomarker

current.cluster.ids <- unique(pbmc.markers$cluster) #c(0, 1, 2, 3, 4, 5, 6, 7)
top1 <- pbmc.markers %>% group_by(cluster) %>% top_n(1, avg_logFC)
new.cluster.ids <- top1$gene #c("CD4 T cells", "CD14+ Monocytes", "B cells", "CD8 T cells", "FCGR3A+ Monocytes", "NK cells", "Dendritic cells", "Megakaryocytes")

pbmc@ident <- plyr::mapvalues(x = pbmc@ident, from = current.cluster.ids, to = new.cluster.ids)
TSNEPlot(object=pbmc, do.label=T, pt.size=0.5) 

Cell Sub-clusters

Further subdivisions within cell types.
If you perturb some of our parameter choices above (for example, setting resolution=0.8 or changing the number of PCs), you might see the CD4 T cells subdivide into two groups. You can explore this subdivision to find markers separating the two T cell subsets. However, before reclustering (which will overwrite object@ident), we can stash our renamed identities to be easily recovered later.

Assign Identity

# First lets stash our identities for later
pbmc <- StashIdent(object = pbmc, save.name = "ClusterNames_0.6")

# Note that if you set save.snn=T above, you don't need to recalculate the
# SNN, and can simply put: pbmc <- FindClusters(pbmc,resolution = 0.8)
pbmc <- FindClusters(object = pbmc, reduction.type = "pca", dims.use = 1:10, 
    resolution = 0.8, print.output = FALSE)

## Warning in BuildSNN(object = object, genes.use = genes.use, reduction.type
## = reduction.type, : Build parameters exactly match those of already
## computed and stored SNN. To force recalculation, set force.recalc to TRUE.

## Warning in BuildSNN(object = object, genes.use = genes.use, reduction.type
## = reduction.type, : Build parameters exactly match those of already
## computed and stored SNN. To force recalculation, set force.recalc to TRUE.

# Demonstration of how to plot two tSNE plots side by side, and how to color
# points based on different criteria
plot1 <- TSNEPlot(object = pbmc, do.return = TRUE, no.legend = TRUE, do.label = TRUE, label.size=labSize)
plot2 <- TSNEPlot(object = pbmc, do.return = TRUE, group.by = "ClusterNames_0.6", 
                  no.legend = TRUE, do.label = TRUE, label.size=labSize)
plot_grid(plot1, plot2)

Find Markers

# Find discriminating markers
tcell.markers <- FindMarkers(object = pbmc, ident.1 = 0, ident.2 = 1)

# Most of the markers tend to be expressed in C1 (i.e. S100A4). However, we
# can see that CCR7 is upregulated in C0, strongly indicating that we can
# differentiate memory from naive CD4 cells.  cols.use demarcates the color
# palette from low to high expression
FeaturePlot(object = pbmc, features.plot = top1$gene, cols.use = c("green", "blue"))

pbmc <- SetAllIdent(object = pbmc, id = "ClusterNames_0.6") 

saveRDS(pbmc, file = "Data/cd14-processed.rds")